Unveiling the Android App MarketĀ¶
Google Play Store Data AnalysisĀ¶
This project aims to analyze the Google Play Store app data to identify key trends, understand user preferences, and explore market dynamics. The analysis focuses on the following objectives:
Correct data types, handle missing values, and eliminate duplicates to ensure data accuracy.
Examine the distribution of app categories and investigate key metrics such as ratings, size, popularity, and pricing.
Analyze user reviews to gauge sentiment, focusing on identifying positive, negative, and neutral feedback.
Generate both static and interactive plots to communicate insights effectively and make the analysis more intuitive.
Based on the analysis, provide data-driven suggestions for optimizing app features, pricing strategies, and user engagement.
Dataset Link:Ā¶
The dataset used for this analysis can be downloaded from Kaggle:
https://www.kaggle.com/datasets/utshabkumarghosh/android-app-market-on-google-play
Dataset Loading & PreparationĀ¶
InĀ [2]:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
InĀ [3]:
apps = pd.read_csv("apps.csv")
InĀ [4]:
review = pd.read_csv("user_reviews.csv")
Data ExplorationĀ¶
InĀ [6]:
review.head(3)
Out[6]:
| App | Translated_Review | Sentiment | Sentiment_Polarity | Sentiment_Subjectivity | |
|---|---|---|---|---|---|
| 0 | 10 Best Foods for You | I like eat delicious food. That's I'm cooking ... | Positive | 1.00 | 0.533333 |
| 1 | 10 Best Foods for You | This help eating healthy exercise regular basis | Positive | 0.25 | 0.288462 |
| 2 | 10 Best Foods for You | NaN | NaN | NaN | NaN |
InĀ [7]:
apps.head(3)
Out[7]:
| Unnamed: 0 | App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Photo Editor & Candy Camera & Grid & ScrapBook | ART_AND_DESIGN | 4.1 | 159 | 19.0 | 10,000+ | Free | 0 | Everyone | Art & Design | January 7, 2018 | 1.0.0 | 4.0.3 and up |
| 1 | 1 | Coloring book moana | ART_AND_DESIGN | 3.9 | 967 | 14.0 | 500,000+ | Free | 0 | Everyone | Art & Design;Pretend Play | January 15, 2018 | 2.0.0 | 4.0.3 and up |
| 2 | 2 | U Launcher Lite ā FREE Live Cool Themes, Hide ... | ART_AND_DESIGN | 4.7 | 87510 | 8.7 | 5,000,000+ | Free | 0 | Everyone | Art & Design | August 1, 2018 | 1.2.4 | 4.0.3 and up |
InĀ [8]:
apps.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 9659 entries, 0 to 9658 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Unnamed: 0 9659 non-null int64 1 App 9659 non-null object 2 Category 9659 non-null object 3 Rating 8196 non-null float64 4 Reviews 9659 non-null int64 5 Size 8432 non-null float64 6 Installs 9659 non-null object 7 Type 9659 non-null object 8 Price 9659 non-null object 9 Content Rating 9659 non-null object 10 Genres 9659 non-null object 11 Last Updated 9659 non-null object 12 Current Ver 9651 non-null object 13 Android Ver 9657 non-null object dtypes: float64(2), int64(2), object(10) memory usage: 1.0+ MB
InĀ [9]:
apps.shape
Out[9]:
(9659, 14)
InĀ [10]:
apps.duplicated().sum()
Out[10]:
0
InĀ [11]:
apps.isna().sum()
Out[11]:
Unnamed: 0 0 App 0 Category 0 Rating 1463 Reviews 0 Size 1227 Installs 0 Type 0 Price 0 Content Rating 0 Genres 0 Last Updated 0 Current Ver 8 Android Ver 2 dtype: int64
InĀ [12]:
apps.describe()
Out[12]:
| Unnamed: 0 | Rating | Reviews | Size | |
|---|---|---|---|---|
| count | 9659.000000 | 8196.000000 | 9.659000e+03 | 8432.000000 |
| mean | 5666.172896 | 4.173243 | 2.165926e+05 | 20.395327 |
| std | 3102.362863 | 0.536625 | 1.831320e+06 | 21.827509 |
| min | 0.000000 | 1.000000 | 0.000000e+00 | 0.000000 |
| 25% | 3111.500000 | 4.000000 | 2.500000e+01 | 4.600000 |
| 50% | 5814.000000 | 4.300000 | 9.670000e+02 | 12.000000 |
| 75% | 8327.500000 | 4.500000 | 2.940100e+04 | 28.000000 |
| max | 10840.000000 | 5.000000 | 7.815831e+07 | 100.000000 |
Data CleaningĀ¶
InĀ [14]:
replace_char = ['+', ',', '$']
Clean_Columns = ['Installs','Price']
for col in Clean_Columns:
for char in replace_char:
apps[col]= apps[col].astype(str).str.replace(char,'')
apps[col] = pd.to_numeric(apps[col])
InĀ [15]:
apps['Last Updated'] = pd.to_datetime(apps['Last Updated'])
InĀ [17]:
# Fill with mean
apps['Rating_mean_fill'] = apps['Rating'].fillna(apps['Rating'].mean())
# Fill with median
apps['Rating_median_fill'] = apps['Rating'].fillna(apps['Rating'].median())
InĀ [19]:
# Comparison of Mean and Median Filling
plt.figure(figsize=(8, 5))
sns.kdeplot(apps['Rating_mean_fill'], label='Mean Fill', color='blue', linestyle='--')
sns.kdeplot(apps['Rating_median_fill'], label='Median Fill', color='red')
sns.kdeplot(apps['Rating'], label='Original', color='green')
plt.title('Comparison of Mean and Median Filling')
plt.legend()
plt.show()
InĀ [20]:
apps['Rating'] = apps['Rating'].fillna(apps['Rating'].median())
InĀ [25]:
apps.rename(columns={'Unnamed: 0':'Index_id'},inplace=True)
InĀ [32]:
apps.drop('Rating_mean_fill',axis=1,inplace=True)
apps.drop('Rating_median_fill',axis=1,inplace=True)
InĀ [34]:
apps.describe()
Out[34]:
| Index_id | Rating | Reviews | Size | Installs | Price | Last Updated | |
|---|---|---|---|---|---|---|---|
| count | 9659.000000 | 9659.000000 | 9.659000e+03 | 8432.000000 | 9.659000e+03 | 9659.000000 | 9659 |
| mean | 5666.172896 | 4.192442 | 2.165926e+05 | 20.395327 | 7.777507e+06 | 1.099299 | 2017-10-30 19:34:02.074748928 |
| min | 0.000000 | 1.000000 | 0.000000e+00 | 0.000000 | 0.000000e+00 | 0.000000 | 2010-05-21 00:00:00 |
| 25% | 3111.500000 | 4.000000 | 2.500000e+01 | 4.600000 | 1.000000e+03 | 0.000000 | 2017-08-05 12:00:00 |
| 50% | 5814.000000 | 4.300000 | 9.670000e+02 | 12.000000 | 1.000000e+05 | 0.000000 | 2018-05-04 00:00:00 |
| 75% | 8327.500000 | 4.500000 | 2.940100e+04 | 28.000000 | 1.000000e+06 | 0.000000 | 2018-07-17 00:00:00 |
| max | 10840.000000 | 5.000000 | 7.815831e+07 | 100.000000 | 1.000000e+09 | 400.000000 | 2018-08-08 00:00:00 |
| std | 3102.362863 | 0.496397 | 1.831320e+06 | 21.827509 | 5.375828e+07 | 16.852152 | NaN |
Exploratory Data AnalysisĀ¶
InĀ [36]:
apps.head(2)
Out[36]:
| Index_id | App | Category | Rating | Reviews | Size | Installs | Type | Price | Content Rating | Genres | Last Updated | Current Ver | Android Ver | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0 | Photo Editor & Candy Camera & Grid & ScrapBook | ART_AND_DESIGN | 4.1 | 159 | 19.0 | 10000 | Free | 0.0 | Everyone | Art & Design | 2018-01-07 | 1.0.0 | 4.0.3 and up |
| 1 | 1 | Coloring book moana | ART_AND_DESIGN | 3.9 | 967 | 14.0 | 500000 | Free | 0.0 | Everyone | Art & Design;Pretend Play | 2018-01-15 | 2.0.0 | 4.0.3 and up |
InĀ [39]:
# Create a revenue estimate (for paid apps)
apps['Revenue'] = apps['Installs'] * apps['Price']
InĀ [42]:
# Top 10 most expensive apps
apps[['App', 'Category', 'Rating', 'Size', 'Price','Installs','Revenue']].nlargest(10, 'Price')
Out[42]:
| App | Category | Rating | Size | Price | Installs | Revenue | |
|---|---|---|---|---|---|---|---|
| 3469 | I'm Rich - Trump Edition | LIFESTYLE | 3.6 | 7.3 | 400.00 | 10000 | 4000000.0 |
| 3327 | most expensive app (H) | FAMILY | 4.3 | 1.5 | 399.99 | 100 | 39999.0 |
| 3465 | š I'm rich | LIFESTYLE | 3.8 | 26.0 | 399.99 | 10000 | 3999900.0 |
| 4396 | I am rich | LIFESTYLE | 3.8 | 1.8 | 399.99 | 100000 | 39999000.0 |
| 4398 | I am Rich Plus | FAMILY | 4.0 | 8.7 | 399.99 | 10000 | 3999900.0 |
| 4400 | I Am Rich Premium | FINANCE | 4.1 | 4.7 | 399.99 | 50000 | 19999500.0 |
| 4402 | I am Rich! | FINANCE | 3.8 | 22.0 | 399.99 | 1000 | 399990.0 |
| 4403 | I am rich(premium) | FINANCE | 3.5 | 1.0 | 399.99 | 5000 | 1999950.0 |
| 4406 | I Am Rich Pro | FAMILY | 4.4 | 2.7 | 399.99 | 5000 | 1999950.0 |
| 4408 | I am rich (Most expensive app) | FINANCE | 4.1 | 2.7 | 399.99 | 1000 | 399990.0 |
InĀ [46]:
# Apps with extreme installs
apps[['App','Installs']].max()
Out[46]:
App š„ Football Wallpapers 4K | Full HD Backgrounds š Installs 1000000000 dtype: object
InĀ [49]:
# Top 5 categories
apps.groupby('Category')['Category'].value_counts().nlargest(5)
Out[49]:
Category FAMILY 1832 GAME 959 TOOLS 827 BUSINESS 420 MEDICAL 395 Name: count, dtype: int64
InĀ [51]:
# the maximum frequency rating
apps['Rating'].mode()[0]
Out[51]:
4.3
InĀ [53]:
# Compare average ratings of free vs. paid apps
print(apps.groupby('Type')['Rating'].agg(['mean', 'median', 'count']))
mean median count Type Free 4.186050 4.3 8903 Paid 4.267725 4.3 756
InĀ [55]:
# Correlation matrix
apps[['Rating', 'Reviews','Size','Installs', 'Price']].corr()
Out[55]:
| Rating | Reviews | Size | Installs | Price | |
|---|---|---|---|---|---|
| Rating | 1.000000 | 0.050207 | 0.045546 | 0.034307 | -0.018662 |
| Reviews | 0.050207 | 1.000000 | 0.179321 | 0.625165 | -0.007598 |
| Size | 0.045546 | 0.179321 | 1.000000 | 0.134291 | -0.022434 |
| Installs | 0.034307 | 0.625165 | 0.134291 | 1.000000 | -0.009405 |
| Price | -0.018662 | -0.007598 | -0.022434 | -0.009405 | 1.000000 |
Data VisualizationĀ¶
InĀ [58]:
# App Distribution by Category
fig = px.bar(apps['Category'].value_counts().head(5).reset_index(),
x='count', y='Category',
title='Top 5 App Categories',
labels={'count': 'Number of Apps', 'Category': 'Category'},
color='Category', text_auto=True)
fig.show()
InĀ [59]:
# Free vs. Paid App Distribution
fig = px.pie(
apps, names='Type',
title='Free vs. Paid Apps',
hole=0.3,
color='Type',
color_discrete_map={'Free': '#00CC96', 'Paid': '#EF553B'}
)
fig.update_traces(textinfo='percent+label')
fig.show()
InĀ [60]:
# Distribution of App Ratings
fig = px.histogram(apps, x='Rating', nbins=30,title='Distribution of App Ratings')
fig.update_layout(bargap=0.1)
fig.show()
InĀ [61]:
# Which App Categories Have the Highest Competition (Oversaturation)?
fig = px.scatter(
apps.groupby('Category').agg({'App': 'count', 'Rating': 'mean'}).reset_index(),
x='App', y='Rating', size='App', color='Category',
title='Category Competition vs. Average Rating',
labels={'App': 'Number of Apps', 'Rating': 'Avg Rating'}
)
fig.show()
InĀ [62]:
# Are Higher-Priced Apps Rated Better (Quality Perception)?
fig = px.scatter(
apps[apps['Price'] > 0],
x='Price', y='Rating', trendline="ols",
title='Price vs. Rating for Paid Apps (Plotly)'
)
fig.show()
InĀ [63]:
# Which Categories Generate the Most Revenue?
fig = px.bar(
apps[apps['Type'] == 'Paid'].groupby('Category')['Revenue'].sum().reset_index(),
x='Category', y='Revenue', color='Revenue',
title='Total Revenue by Category (Paid Apps)'
)
fig.show()
InĀ [64]:
# Do Certain Genres Have Higher Price Potential?
fig = px.treemap(
apps[apps['Type'] == 'Paid'],
path=['Genres'], values='Price',
title='Price Distribution by Genre (Paid Apps)'
)
fig.show()
InĀ [65]:
# Price Impact on Installs
paid_apps = apps[apps['Price'] > 0]
fig = px.box(paid_apps, x='Price', y='Installs', log_y=True, title='Price vs. Installs (Paid Apps)')
fig.show()
InĀ [66]:
# App Size Impact Installs
fig = px.density_heatmap(
apps, x='Size', y='Installs', nbinsx=20,
title='App Size vs. Installs',
marginal_x="histogram", marginal_y="histogram"
)
fig.show()
InĀ [67]:
# Are Frequent Updates Correlated with Higher Ratings?
apps['Days Since Update'] = (pd.Timestamp.now() - apps['Last Updated']).dt.days
fig = px.scatter(
apps, x='Days Since Update', y='Rating',
trendline="lowess", title='Update Freshness vs. Rating'
)
fig.show()
InĀ [68]:
# Are There Seasonal Trends in App Launches/Updates
apps['Month Updated'] = apps['Last Updated'].dt.month
fig = px.line(
apps['Month Updated'].value_counts().sort_index().reset_index(),
x='Month Updated', y='count',
title='App Updates by Month (Seasonality)'
)
fig.show()
InĀ [70]:
# Target underserved age groups
fig = px.pie(
apps, names='Content Rating',
title='Market Share by Content Rating',
hole=0.4
)
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()
InĀ [98]:
plt.figure(figsize=(10, 4))
sns.regplot(data=apps, x='Reviews', y='Installs', scatter_kws={'alpha':0.3})
plt.xscale('log')
plt.yscale('log')
plt.title('Reviews vs. Installs')
plt.show()
InĀ [75]:
# App Size vs. Installs
fig = px.scatter(
apps, x='Size', y='Installs',
title='App Size vs. Installs',
trendline='lowess',
color='Type',
log_y=True
)
fig.show()
InĀ [76]:
# Sentiment Distribution
fig1 = px.bar(
review['Sentiment'].value_counts().reset_index(),
x='count', y='Sentiment',
title='Sentiment Distribution'
)
fig1.show()
InĀ [81]:
# Get the top 5 apps based on the number of reviews
top_5_apps = review['App'].value_counts().nlargest(5).index
filtered_df = review[review['App'].isin(top_5_apps)]
# Sentiment Polarity by Top 10 Apps Plot
fig2 = px.bar(
filtered_df, x='App', y='Sentiment_Polarity', color='Sentiment',
title='Sentiment Polarity by Top 10 Apps')
fig2.show()